核反应堆冷却剂系统智能事故诊断模型研究  

Research on Intelligent Accident Diagnosis Model of Nuclear Reactor Coolant System

作　　者：闫家胜 隋阳 戴滔 刘家义 金艺 贾晓龙 Yan Jiasheng;Sui Yang;Dai Tao;Liu Jiayi;Jin Yi;Jia Xiaolong(School of Nuclear Science and Technology,University of South China,Hengyang,Hunan,421001,China;Key Laboratory of Advanced Nuclear Energy Design and Safety,Ministry of Education,Hengyang,Hunan,421001,China;Fujian Fuqing Nuclear Power Co.,Ltd.,Fuqing,Fujian,350300,China)

机构地区：[1]南华大学核科学技术学院,湖南衡阳421001 [2]先进核能技术设计与安全教育部重点实验室,湖南衡阳421001 [3]福建福清核电有限公司,福建福清350300

出　　处：《核动力工程》2025年第2期282-292,共11页Nuclear Power Engineering

基　　金：国家自然科学基金(52174189);湖南省杰出青年科学基金(2023JJ10035)。

摘　　要：尽管人工智能技术在核电厂的事故诊断领域中已被广泛应用,但传统模型存在诊断准确性不足、泛化性较弱等缺陷,难以满足核反应堆冷却剂系统(NRCS)对于事故诊断的高要求。本研究建立了一种NRCS智能事故诊断新模型。首先,为提高模型事故诊断的准确性,应用了卷积神经网络(CNN)和门控循环单元(GRU),结合CNN强大的特征提取能力和GRU高效的时序数据分类能力,建立了NRCS事故诊断模型(CNN-GRU模型);其次,为提高模型的泛化性,应用灰狼优化(GWO)算法,在CNN-GRU模型中自适应优化超参数,建立了NRCS智能事故诊断模型(GWO-CNN-GRU模型);最后,为验证所提出模型的性能,本研究以核电厂仿真与严重事故分析仪(PCTRAN)中的NRCS为研究对象,模拟测试了1种正常工况和4种典型事故工况的诊断过程。结果显示,在CPR1000堆型的NRCS测试集上,所提出模型的事故诊断平均准确率为99.6%,相较于GRU和CNN-GRU模型分别提高了2.1%和1.5%;同时,在AP1000堆型的NRCS测试集上,所提出模型的事故诊断平均准确率为99.5%,相较于其他两种模型分别提高了1.7%和1.3%。因此,本文提出的模型在准确性和泛化性方面均表现出优异性能,为NRCS智能事故诊断提供了重要参考。Although artificial intelligence technology has been extensively employed in the field of accident diagnosis for nuclear power plants,conventional models often suffer from shortcomings such as insufficient accuracy and poor generalizability,which fail to meet the stringent requirements for accident diagnosis of the nuclear reactor coolant system(NRCS).This study establishes a new intelligent accident diagnosis model for NRCS.Firstly,to enhance the accuracy of accident diagnosis,an NRCS accident diagnosis model(CNN-GRU)integrating convolutional neural network(CNN)and gated recurrent unit(GRU)is proposed;Firstly,to enhance the diagnostic accuracy of the model,convolutional neural networks(CNN)and gated recurrent unit(GRU)were integrated.The powerful feature extraction capabilities of CNN and the efficient time-series data classification abilities of GRU were combined to establish the NRCS accident diagnosis model(CNN-GRU).Secondly,to enhance the generalizability of the model,the grey wolf optimizer(GWO)algorithm was used to adaptively optimize the hyperparameters within the CNN-GRU model,thereby establishing the NRCS intelligent accident diagnosis model(GWOCNN-GRU).Finally,to validate the performance of the proposed model,the NRCS in personal computer transient analyzer(PCTRAN)was used as the object of study,and the diagnostic process of one normal operating condition and four typical accident conditions was simulated.The results demonstrated that the proposed model achieved an average accident diagnosis accuracy of 99.6%on the NRCS test set for the CPR1000 reactor type,which is an improvement of 2.1%and 1.5%compared to the GRU and CNN-GRU models,respectively;Similarly,on the NRCS test set for the AP1000 reactor type,the proposed model achieved an average accident diagnosis accuracy of 99.5%,representing an increase of 1.7%and 1.3%over the other two models,respectively.Therefore,the model proposed in this paper demonstrates superior performance in terms of accuracy and generalizability,providing a valuable referenc

关 键 词：核反应堆冷却剂系统(NRCS) 智能事故诊断 卷积神经网络(CNN) 门控循环单元(GRU) 灰狼优化(GWO)算法 

分 类 号：TL383[核科学技术—核技术及应用]